Florian Hellmeier, Kay Brosien, Carsten Eickhoff, Alexander Meyer
{"title":"超越一次性验证:基于预测和诊断的人工智能医疗设备的自适应验证框架","authors":"Florian Hellmeier, Kay Brosien, Carsten Eickhoff, Alexander Meyer","doi":"arxiv-2409.04794","DOIUrl":null,"url":null,"abstract":"Prognostic and diagnostic AI-based medical devices hold immense promise for\nadvancing healthcare, yet their rapid development has outpaced the\nestablishment of appropriate validation methods. Existing approaches often fall\nshort in addressing the complexity of practically deploying these devices and\nensuring their effective, continued operation in real-world settings. Building\non recent discussions around the validation of AI models in medicine and\ndrawing from validation practices in other fields, a framework to address this\ngap is presented. It offers a structured, robust approach to validation that\nhelps ensure device reliability across differing clinical environments. The\nprimary challenges to device performance upon deployment are discussed while\nhighlighting the impact of changes related to individual healthcare\ninstitutions and operational processes. The presented framework emphasizes the\nimportance of repeating validation and fine-tuning during deployment, aiming to\nmitigate these issues while being adaptable to challenges unforeseen during\ndevice development. The framework is also positioned within the current US and\nEU regulatory landscapes, underscoring its practical viability and relevance\nconsidering regulatory requirements. Additionally, a practical example\ndemonstrating potential benefits of the framework is presented. Lastly,\nguidance on assessing model performance is offered and the importance of\ninvolving clinical stakeholders in the validation and fine-tuning process is\ndiscussed.","PeriodicalId":501112,"journal":{"name":"arXiv - CS - Computers and Society","volume":"11 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Beyond One-Time Validation: A Framework for Adaptive Validation of Prognostic and Diagnostic AI-based Medical Devices\",\"authors\":\"Florian Hellmeier, Kay Brosien, Carsten Eickhoff, Alexander Meyer\",\"doi\":\"arxiv-2409.04794\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Prognostic and diagnostic AI-based medical devices hold immense promise for\\nadvancing healthcare, yet their rapid development has outpaced the\\nestablishment of appropriate validation methods. Existing approaches often fall\\nshort in addressing the complexity of practically deploying these devices and\\nensuring their effective, continued operation in real-world settings. Building\\non recent discussions around the validation of AI models in medicine and\\ndrawing from validation practices in other fields, a framework to address this\\ngap is presented. It offers a structured, robust approach to validation that\\nhelps ensure device reliability across differing clinical environments. The\\nprimary challenges to device performance upon deployment are discussed while\\nhighlighting the impact of changes related to individual healthcare\\ninstitutions and operational processes. The presented framework emphasizes the\\nimportance of repeating validation and fine-tuning during deployment, aiming to\\nmitigate these issues while being adaptable to challenges unforeseen during\\ndevice development. The framework is also positioned within the current US and\\nEU regulatory landscapes, underscoring its practical viability and relevance\\nconsidering regulatory requirements. Additionally, a practical example\\ndemonstrating potential benefits of the framework is presented. Lastly,\\nguidance on assessing model performance is offered and the importance of\\ninvolving clinical stakeholders in the validation and fine-tuning process is\\ndiscussed.\",\"PeriodicalId\":501112,\"journal\":{\"name\":\"arXiv - CS - Computers and Society\",\"volume\":\"11 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - CS - Computers and Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.04794\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - CS - Computers and Society","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.04794","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Beyond One-Time Validation: A Framework for Adaptive Validation of Prognostic and Diagnostic AI-based Medical Devices
Prognostic and diagnostic AI-based medical devices hold immense promise for
advancing healthcare, yet their rapid development has outpaced the
establishment of appropriate validation methods. Existing approaches often fall
short in addressing the complexity of practically deploying these devices and
ensuring their effective, continued operation in real-world settings. Building
on recent discussions around the validation of AI models in medicine and
drawing from validation practices in other fields, a framework to address this
gap is presented. It offers a structured, robust approach to validation that
helps ensure device reliability across differing clinical environments. The
primary challenges to device performance upon deployment are discussed while
highlighting the impact of changes related to individual healthcare
institutions and operational processes. The presented framework emphasizes the
importance of repeating validation and fine-tuning during deployment, aiming to
mitigate these issues while being adaptable to challenges unforeseen during
device development. The framework is also positioned within the current US and
EU regulatory landscapes, underscoring its practical viability and relevance
considering regulatory requirements. Additionally, a practical example
demonstrating potential benefits of the framework is presented. Lastly,
guidance on assessing model performance is offered and the importance of
involving clinical stakeholders in the validation and fine-tuning process is
discussed.